Local Fisher Discriminant Component Hashing for Fast Nearest Neighbor Classification
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
An Efficient 3D Geometrical Consistency Criterion for Detection of a Set of Facial Feature Points
IEICE - Transactions on Information and Systems
Facial feature localization using weighted vector concentration approach
Image and Vision Computing
Face recognition technology and its real-world application
PerMIn'12 Proceedings of the First Indo-Japan conference on Perception and Machine Intelligence
| Hi-index | 0.00 |
This paper describes a new face recognition method using a projection-based 3D normalization and a shading subspace orthogonalization under variation in facial pose and illumination. The proposed method does not need any reconstruction and reillumination for a personalized 3D model, thus it can avoid these troublesome problems and the recognition process can be done rapidly. The facial size and pose including out of plane rotation can be normalized to a generic 3D model from one still image and the input subspace is generated by perturbed cropped patterns in order to absorb the localization errors. Furthermore, by exploiting the fact that a normalized pattern is fitted to the generic 3D model, illumination robust features are extracted through the shading subspace orthogonalization. Evaluation experiments are performed using several databases and the results show the effectiveness of our method under various facial poses and illuminations.